Reserve site selection for data‐poor invertebrate fisheries using patch scale and dispersal dynamics: a case study of sea cucumber<i>(Cucumaria frondosa)</i>
Bibliographic record
Abstract
ABSTRACT Globally, management decisions for recently established invertebrate fisheries are based on limited information. The negative consequences are being realized as now at least one‐third of invertebrate fisheries are over‐exploited, collapsed or closed. Management of a fishery should be based on abundance and productivity data. This information is often unavailable but in some jurisdictions, spatial distributions are known. A methodology for designating a network of reserves for sedentary invertebrates, using sea cucumber (Cucumaria frondosa) on the Scotian Shelf, Canada is proposed, as a case study. It is assumed that there is a positive relationship between spawner density and per capita population growth rate in sedentary broadcast spawners and that fertilization success declines rapidly at low densities. Protection of high‐density habitat would safeguard critical spawner density, especially when the critical density is unknown. Using spatial distribution, a method to identify size and location of reserves designed to protect high‐density habitat was developed. First, geographically distinct regional‐scale clusters are identified. Within clusters, the characteristic patch scale was determined through spatial autocorrelation analysis. The size of reserves can be set as 50% of each high‐density patch based on an appropriate risk‐averse approach. The allocation of reserve size and location is not dependent on results of a numerical circulation model. However, the analysis was refined by using one to predict larval drift patterns, and connectivity among patches. Reserve boxes were then designated to safeguard from 30 to 65% of each high‐density patch. Marine reserves do not work if inappropriately sited. In the absence of any other information, it is argued that siting reserves on high‐density areas is an essential and appropriate approach to data‐poor invertebrate fisheries management. A high percentage of each high‐density patch could serve as risk‐averse ‘spatial management’ reference points, and could be modified once replenishment rates are known. Copyright © Her Majesty the Queen in Right of Canada 2013
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".